Companies are investing billions in neural networks, seeking solutions to optimize workflows. Autonomous agents have evolved into intelligent assistants—they independently initiate operations, analyze data, make changes in CRM systems, and generate code. Reports from Google and other major tech firms show a 200% increase in productivity. The hype of 2025 is over, replaced by cases with proven ROI—saving up to 86% of time on routine tasks, especially in corporate resource management strategies.
The 2025 Web Summit in Lisbon confirmed the dominance of AI. Startups like Recrewty and Credituz are turning a profit. Recrewty automates HR processes, while Credituz issues loans based on document photos. However, businesses are becoming more cautious: adoption is still gaining momentum, and the real-world impact of many new tools is still being tested.
AI trends in 2026 focus on agentic approaches and infrastructure, with the MCP protocol standing out for integration. The main AI trends for 2026 are multi-agent systems and edge AI. What's trending in AI now? Systems that learn from internal data, minimize hallucinations, provide personalized answers, and scale according to business goals.
The market is approaching a trillion dollars. Several AI platforms are expanding globally, offering flexible API access and supporting voice assistants for information processing. China's Qwen3 is catching up to OpenAI. Businesses face challenges: a shortage of orchestrators, security risks, and complex integration of interconnected systems.
This article analyzes global practices, showcasing case studies, success metrics, and an implementation roadmap. Readers will understand where AI delivers value—in finance, marketing, and analytics. Data-backed projects define market leaders, helping professionals find the best applications in their fields.
Artificial intelligence is a defining trend of 2026, the zeitgeist of the current business era. Companies are investing in neural networks to optimize processes across various work tasks. Autonomous agents now independently handle operations like procurement, data analysis, and CRM updates. The 2025 Lisbon Web Summit confirmed AI's dominance. Startups like Credituz and Nero Budget are becoming profitable, while giants like Google and other global tech firms are scaling factories for generative content creation.
Credituz is digitizing mortgages, connecting banks and developers via photo documents, achieving faster speeds than other solutions. Nero Budget coaches SMEs on expenses, yielding up to 20% savings in resource management. But business remains cautious: implementation is just beginning, and ROI is being rigorously tested. Many projects start as hypotheses that need to be proven with data in real-world applications.
The market is marching towards a trillion dollars. Cloud-based GPU factories from major providers deliver energy efficiency gains of up to 105,000x. A Google report notes the shift from hype to practice. Agents are being integrated via the MCP protocol—a new standard for IT infrastructure. Leading AI studios worldwide now offer thousands of agent templates for HR and analytics, including specialized bots for voice interactions.
China is leading with Qwen3, where open models support cutting-edge algorithms. Edge AI on devices enhances data privacy. The Web Summit showcased cases: Recrewty reducing headcount by 30%, Microsoft introducing SOC agents to combat attacks. Regional pilot projects across Europe and Asia are advancing rapidly, with many paying back in months thanks to cloud services.
AI is losing its novelty status. Businesses are adopting a critical view: where is the real value in information processing? Implementation is now focused on metrics. New roles—orchestrators—are in high demand for professional support. This trend defines market leaders, helping major players integrate AI into their mass-market development plans.
Success metrics for AI projects are the foundation of business decisions. ROI is calculated based on time and cost savings in daily operations. AI agents reduce routine work by 86%. HR departments have seen headcount reductions of 30%. Case studies from the Web Summit provide benchmarks for real-world application.
Credituz accelerated loan processing 10x—approval via document photos became a key function helping them achieve goals faster. Nero Budget cuts SME expenses by 20% through financial data analysis. Major AI platforms report client retention rates exceeding 95%. Agent accuracy reaches 98% when grounded on internal data.
Implementation is measured simply. GPU and training costs are the initial inputs; profit from automation is the output. Pilots pay for themselves in 3 months. Hallucinations drop to 2% on internal data, especially in tasks involving text generation and idea creation.
SOC agents block 99% of attacks in corporate networks. Productivity increases by factors of 105,000x on NVIDIA-powered factories. Businesses choose trends based on data—without it, adoption stalls, even though the need for intelligent daily assistants is clear.
The top 5 AI trends for 2026 represent the business zeitgeist in the tech sector. Global analytics reports from Google and other major research firms analyze the shifts in technology application. Agents dominate, and infrastructure scales for information processing. Companies seek ROI in real-world content creation cases. Web Summit 2025 showed the transition from hype to profit in operational workflows. We break down the key directions with examples and metrics—highlighting benefits for businesses, especially in support and management.
Agentic AI leads as the intelligent assistant. Factories accelerate computing power. Security becomes autonomous with algorithmic checks. Edge-models reduce the load on cloud services. Upskilling creates new professional roles. The trends are backed by numbers: 86% savings, months-long payback periods for major users.
Below is a detailed breakdown of each trend, providing businesses with an implementation roadmap to identify goals and make informed decisions aligned with modern developments.
Agentic AI is the main trend of 2026, the defining spirit for modern businesses. Companies are moving from simple chatbots to systems that independently plan tasks and execute complex chains of actions, such as writing documentation. A Google report calls the specialists managing them “shepherds of neural networks.” They control autonomous agents via the MCP protocol, connecting CRMs, databases, and external services for text generation.
The 2025 Lisbon Web Summit confirmed this shift. Startups like Recrewty and Credituz are already monetizing agents. Recrewty fills vacancies without an HR team, saving 30% on resources. Credituz issues loans based on document photos with 95% accuracy, positioning them as leaders. The hype is fading; business demands KPIs: ROI in 3 months, 86% reduction in routine tasks.
Leading AI platforms now offer hundreds of ready‑made agents for global markets. They analyze data, generate code, and optimize marketing with specialized assistants. Hallucination risks are minimized by grounding agents on internal data. Implementation starts with pilots, verifying the impact with hard numbers.
Agents are changing job roles. Orchestrators now coordinate multi‑agent systems in professional settings. Training courses are appearing in regions to foster participation. This trend is valuable in finance, HR, and analytics—especially for large enterprises that can create personalized solutions. Businesses testing now will seize leadership through robust network support.
AI factories are a key trend for scaling in 2026 corporate plans. Companies are building GPU clusters for digital content creation. Major global tech enterprises are leading the wave, with factories processing billions of requests, powering ads and news. NVIDIA enables energy efficiency gains of up to 105,000x. Costs fall, speeds rise on existing networks. Businesses are shifting from the cloud to private data centers to achieve mass adoption.
LoRA simplifies model fine-tuning—an open feature for cloud services. Companies adapt AI to their internal data. The result: 98% accuracy, minimal hallucinations in generation modes. A Google report highlights this shift towards general efficiency. Factories integrate agents via MCP, with scale paying off within a year.
One notable case study shows how enterprises with 40,000 clients use AI factories for analytics, gaining insights for design and video production. Employee cost savings reach 50% on certain tasks. Startups are even building edge-factories on devices. Dependence on OpenAI is decreasing. China's DeepSeek provides a blueprint—models running locally, especially for contact center applications.
Risks are managed: energy consumption, data security. Solutions include green GPUs and SOC agents for monitoring. This trend is reshaping the market. Businesses with their own AI factories gain a competitive edge in areas requiring full optimization and control.
AI security is a major trend in 2026, driven by the proliferation of agents in corporate environments. Attacks on neural networks are increasing—especially those involving text and image generation. Microsoft's SOC agents detect threats in real-time, blocking 99% of incidents and helping achieve security goals without added risk. Companies protect data without needing to hire more IT staff.
A real-world case: prompt-injection attacks on chat agents. The solution is an AI-SAFE framework for modern systems. Grounding agents on verified sources reduces hallucination risks. A Google report warns: hallucinations in security are unacceptable, especially after news of breaches.
Global enterprises are actively implementing these measures. Leading tech platforms integrate SOC agents into their infrastructure, achieving 98% monitoring accuracy. Regulations demand compliance. AI now automatically checks privacy policies across voice and text communications.
This trend changes operational processes: agents protect agents from other threats like DDoS. Edge AI minimizes data leaks on devices. Companies save up to 40% on audit costs. Security is becoming a competitive advantage and a key part of development strategy.
Vibecoding is a 2026 development trend accelerating IT projects. AI writes code from natural language descriptions. Tools like SourceCraft generate applications using open algorithms. Developers focus on architecture and planning. Google notes a 5x growth in low-code platforms, enabling higher quality than competitors.
The DeepSeek case: models generate complex, error-free code. LoRA allows fine-tuning for specific cloud projects. Time savings on code reviews reach 70%. Qwen3 competes directly with GitHub Copilot, with 96% accuracy in professional tasks.
Global development teams leverage leading AI coding platforms. Agents integrate code into CRMs and support voice queries. Startups accelerate product launches with modern assistants. Risks include logical hallucinations, but grounding solves this for daily development.
This trend simplifies dev processes. Teams become less dependent on junior staff, freeing up resources. Organizations ship features faster at scale. Implementation starts with pilots, showing ROI within a quarter.
Upskilling is a critical 2026 trend addressing the need for professional training in AI. Agents are creating new roles like “orchestrators” within corporate teams. These specialists manage multi-agent systems handling concurrent tasks. Google predicts a 50% demand increase in the IT sector. Organizations are retraining employees to participate in technological development.
Case studies from global enterprises show HR staff reduced by 30%, creating demand for "shepherds of neural networks" to support specialized functions. Leading tech companies offer training courses in MCP, grounding, and hallucination monitoring, building skills in months and significantly improving solution quality.
Programs are launching across global markets to train specialists in creating personal AI assistants. Roles like “neuro-lawyers” and AI-analysts are in high demand. The Web Summit showed that organizations investing in upskilling lead in mass AI adoption. The effect: project ROI increases by +40%.
This trend solves the talent shortage in operational workflows. Developers evolve into coordinators for the broader AI strategy. Organizations investing in training see payback through productivity gains.
Real-world AI implementation cases offer crucial lessons for business. The 2025 Web Summit showcased examples from major players. Startups and corporations shared metrics for general strategy. We analyze successful practices—from pilot to scale—in creating digital products.
AI risks grow with scale in corporate strategies. Hallucinations can lead to incorrect decisions in content generation. Prompt-injections can compromise agents via voice or text queries, leading to data leaks from websites and contacts. A Google report warns: without control, the effect of AI is zero in operational areas.
The key lesson from case studies: run pilots with clear KPIs. Continuous risk monitoring must be part of any implementation plan. By using grounding and SOC agents, companies can navigate these pitfalls and achieve market leadership.
It's time for businesses to implement AI. This 2026 roadmap addresses optimization in key operational areas. Step 1: Audit and Pilot (Month 1). Start by auditing your processes. Identify routine tasks in marketing, HR, or analytics. Choose a pilot—one agent for creating personalized texts or images. The Web Summit teaches: start small, measure KPIs daily. Use leading AI platforms to integrate ready-made assistants via MCP for voice or text support. Target the 86% time savings seen in cases like Credituz.
Step 2: Grounding and Security (Month 2). Implement grounding to minimize hallucinations to below 2% on internal data. Add AI security agents to block 99% of potential attacks. Leverage AI factories to accelerate processing.
Step 3: Upskilling (Month 3). Train your first “orchestrators” within 2 months. Equip them with skills in MCP, grounding, and multi-agent coordination. Case studies from global enterprises show that while headcount in some areas may decrease, overall productivity can increase by +40%.
Step 4: Scale with Edge AI (Month 4+). Fine-tune models locally using LoRA. This reduces dependency on cloud providers and can save up to 40% on costs, inspired by the independence shown by solutions like China's DeepSeek.
This roadmap can deliver ROI within a quarter. Companies that follow a structured plan will capture market share, turning AI into a true strategic partner.
In 2026, artificial intelligence is transforming from a simple tool into a true business partner for daily tasks. Autonomous agents plan operations, AI factories scale computing power, and AI-driven security protects corporate data from attacks. Trends highlighted by Google and the Web Summit confirm: ROI can reach 86% savings, and pilots can pay back in months.
Global businesses are exceeding expectations in the AI space. Leading AI platforms serve tens of thousands of clients with agents for generating text, images, and ideas. China's Qwen3 is shifting the balance of power with open models. Companies that adopt this roadmap now—auditing processes, running pilots, and investing in upskilling—will capture the market.
AI solves critical pain points: talent shortages, operational routine, and hallucination risks—through grounding and SOC agents. The AI trends of 2026 are dictating the future; adaptation is essential for leadership.
Start with one agent. You'll see the impact within a quarter. Partnering with neural networks is what defines the leaders of this new era, helping professionals achieve their most important goals.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Can you create web services and applications without deep programming knowledge? With the advent of powerful language models and AI assistants — yes. All you need is to clearly formulate the task. This approach is called vibecoding (vibe coding).
It gained particular popularity after OpenAI co-founder Andrej Karpathy publicly demonstrated in February 2025 how he fully delegates programming to neural network agents. His workflow requires almost no manual code input. He formulates an idea — the model writes, checks, and refines the project.
In this article, we will:
Our goal in this material is not just to describe the trend, but to give a practical understanding of how to use vibe coding in work or business, what limitations and opportunities it offers, and why this direction is becoming part of the future of technology.
Vibecoding (vibe coding) is a programming style where the developer does not write code manually, but describes the task in natural language, and artificial intelligence itself creates the working code. This approach lowers the technical barrier: there's no need to know language syntax, understand architecture, or manually debug the project — these tasks are performed by an AI assistant.
This approach is called "code by vibe" because the basis is not compiler logic, but the context, intent, and result that the developer describes as a thought, goal, or command.
The term "vibecoding" was introduced by Andrej Karpathy — a scientist, developer, and co-founder of OpenAI. In 2025, he described his methodology where the code is not important, the result is, and the entire process can be delegated to AI.
"I don't touch the keyboard. I say: 'reduce the left indents by half' — and the agent does everything itself. I even process errors through chat, without diving in."
-- Andrej Karpathy, February 2025
He claims that development becomes similar to managing an interface through dialogue, rather than writing lines manually. For example, his project MenuGen (a web service that generates dish images from a menu photo) is completely written by AI: from authorization to the payment system.
To start using vibecoding, you need an editor or development environment with AI support. Below is a list of popular tools in 2025 that allow you to generate code, create applications, fix errors, and run projects directly in the browser or on a local machine.
Junie – Assistant for code snippets. AI Assistant – Programming chat.
You can connect different language models in each vibecoding tool. But not all are equally good with code. Some are better for text generation, others for development, others for bug fixing and API work.
For a quick guide, here is a comparison of the most popular models for vibecoding:
| Model | Suitable for | Advantages | Restrictions | Where is it used | | ------ | ------ | ------ | ------ | ------ | | GPT‑4o | Daily tasks, routine code | Stable, fast, understands prompts well | Limited context window | Cursor, Replit, JetBrains AI | | GPT‑4.1 | Full-scale programming | Deep analysis, creates architecture | Slower, more expensive | Devin AI, Cursor (Pro, Ultra) | | Claude Code (Opus 4) | Code generation & refactoring | Writes excellent code | CLI interface, not for beginners | Claude Code CLI | | DeepSeek-Coder | Research, structural tasks | Generates complex queries and SQL | Less known, unstable | Cursor, via Cline | | Gemini (Google) | Web interfaces, API integration | Strong logic, API knowledge | Can "hallucinate" | Via Cline or Replit | | GPT‑3.5-turbo | Quick prototypes, pet projects | Lightweight, cheap, good with basic tasks | Weak on architecture and complex logic | Free mode in Cursor, Replit |
The fastest way to understand vibecoding is to try it yourself. Below is a step-by-step guide on how to create a Telegram bot that, given a link to a GitHub repository, sends a brief summary: name, author, stars, release, and other data.
We'll use the Cursor editor with the GPT‑3.5 model. Everything is done right in the editor — no manual coding required.
Step 1: Set up the environment. Install Cursor, choose a plan (Pro recommended for full access), and enable Agent mode with the GPT‑3.5 model. Step 2: Describe the task. Formulate a clear prompt in the chat, specifying the bot's function, language (Python), and libraries (Aiogram, requests). Step 3: Generate the project. The AI assistant creates the project structure: bot.py, requirements.txt, README.md, .env.example. Step 4: Correct errors. If errors appear when running, copy the terminal text into the chat with the words: "Fix the errors." The AI will make corrections. Step 5: Launch. Run the bot with python bot.py. It will successfully start and respond to links in Telegram. Step 6: Study and improve. The finished project can be uploaded to GitHub, deployed (e.g., via Replit), and extended with features.
✅ Advantages:
❌ Disadvantages:
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Midjourney’s Omni Reference is a new technology available in Version 7, enabling users to precisely control image generation using artificial intelligence (AI). With Omni Reference, you can now use it as a professional tool: add an image reference, adjust the oref parameter, control omni weight (ow), and achieve stable, predictable results.
In this article, we break down Omni Reference features, explain ow values, provide step-by-step instructions, and share practical use cases for real projects.
AI image generation has advanced rapidly, but users have long faced challenges in controlling outputs—characters would change, objects would shift, and styles wouldn’t remain consistent.
Now you can precisely define the influence of a reference image, controlling facial features, clothing, style, visual elements, and details. This is especially important for projects requiring consistent visuals—whether for websites, marketing materials, or video content.
What Is Omni Reference and How Does It Work?
Omni Reference is a system that analyzes a source image and extracts key characteristics:
This data is then used by the AI during generation. Omni Reference doesn’t just copy an image—it adapts it to fit a new prompt. This ensures a balance between creativity and accuracy.
Previously, Midjourney offered Character Reference, which worked mainly with characters. The key difference is that Omni Reference is broader and covers multiple aspects.
| Capability | Character Reference | Omni Reference |
|---|---|---|
| Characters | Yes | Yes |
| Objects | No | Yes |
| Face & Clothing | Limited | Yes |
| Style | Partial | Yes |
| Multiple Objects | No | Yes |
| Textures & Backgrounds | No | Yes |
Omni Reference significantly expands Midjourney’s visual control capabilities.
Key features of Omni Reference include:
These features make Midjourney more than just a generator—it becomes a full-fledged image creation system.
The oref parameter is the URL of the image used as a reference. All image links must be publicly accessible.
Example query: /imagine prompt futuristic character --oref https://site.com/image.jpg
Omni weight (ow) determines how strongly the reference influences the generated image. The default value is 1000, but fine-tuning unlocks its full potential.
ow Value Ranges: Low, Medium, and High
Important note: High ow values provide control but may reduce variety.
This beginner-friendly guide will get you started:
Tip: Start with a low ow value and gradually increase it.
Texture Generation and Consistency Maintenance
With Omni Reference, texture generation is now a controlled process. You can create complex patterns and apply styles across different objects while maintaining visual integrity.
Now you can:
Example
An online clothing store used Omni Reference to generate 64 t-shirt variations from a single fabric photo. Result: unified style and reduced budget.
Strategies for Improving Result Accuracy
To maximize precision:
Business Case Study
A coffee chain used Midjourney Omni Reference with ow = 400. The outcome: a unified visual style and an approximate 15% reduction in marketing costs.
Omni Reference Applications for Various Tasks
Omni Reference can be used for:
Even experimental models (like “nano banana”) suggest that Omni Reference will continue to expand in application.
Midjourney’s Omni Reference is a key tool in Version 7, elevating image generation to a professional level. It provides control, precision, and result stability.
If you regularly work with visuals, start using Omni Reference now. Experiment with ow values, combine multiple references, add complementary parameters, and unlock the full potential of Midjourney’s AI.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Choosing between the two leading neural networks determines the efficiency of working with information in 2026. Chinese DeepSeek and American ChatGPT offer different architectures, prices, and capabilities. One model costs 4.5 times less, the other has a larger context window. The difference lies in user accessibility, text generation speed, and data processing approaches. This article answers the questions: which neural network to choose for specific tasks, where each model performs better, and what are the pros and cons of each solution. The comparison is based on performance tests, developer feedback, and architectural analysis.
The choice between neural networks depends not on abstract characteristics, but on specific tasks. Six factors determine which model to use for work.
Table: 6 Key Differences Between DeepSeek and ChatGPT
| Criterion | DeepSeek | ChatGPT | Practical Significance |
|---|---|---|---|
| Architecture | Mixture-of-Experts (MoE) | Dense Transformer | 60% resource savings |
| API Cost | $0.28/1M tokens | $1.25/1M tokens | Saves $9700 on 10k request |
| Context Window | 128K tokens | 200K tokens | Handles 300-page documents |
| Coding Quality | 97% success rate | 89% success rate | Generates working code on first try |
| Code Openness | MIT License | Proprietary | Enables local deployment |
DeepSeek is built on Mixture-of-Experts (MoE). The system contains 256 experts. For each request, 8-9 experts are activated. This provides 671 billion parameters but utilizes only 37 billion. ChatGPT uses Dense architecture. All 1.8 trillion parameters work on every request. The difference in power consumption reaches 60%. MoE architecture processes requests 2-3 times faster for specialized tasks. Falls short in universality.
Table: Architecture Comparison
| Parameter | DeepSeek (MoE) | ChatGPT (Dense) | Advantage |
|---|---|---|---|
| Total Parameters | 671B | 1.8T | Lower infrastructure costs |
| Active Parameters | 37B (5.5%) | 1.8T (100%) | Selective activation |
| Power Consumption | 40% of Dense | 100% | 60% savings |
| Specialized Task Speed | +200-300% | Baseline | Faster for code and math |
| Universal Task Speed | -10-15% | Baseline | Lag in general questions |
| GPU Memory | 80GB for R1 | 320GB for version | Less memory required |
This architecture allows DeepSeek to spend less on servers. Users get free access without limits. For coding and math tasks, this delivers better results. For general text generation, the difference is less noticeable.
DeepSeek-V3.2 API costs $0.028 per 1 million tokens with caching and $0.28 on cache misses. ChatGPT-5 charges $0.025 per 1 million tokens in the base plan, but advanced o3-mini models cost $1.25. Training DeepSeek V3 cost $5.6 million. ChatGPT-5 required investments exceeding $100 million. DeepSeek offers completely free access without restrictions. For businesses with 10,000 monthly requests, API savings amount to $9700 when using cache. DeepSeek caching provides 90% savings on repeated requests.
Table: 2025 Implementation Cost Comparison
| Component | DeepSeek V3.2 | ChatGPT-5/o3-mini | Difference |
|---|---|---|---|
| Price per 1M tokens (cache) | $0.028 | $0.025 (GPT-5) | Comparable |
| Price per 1M tokens (no cache) | $0.28 | $1.25 (o3-mini) | 4.5x more expensive |
| Training Cost | $5.6M | $100M+ | 17.8x more expensive |
| Monthly Plan | $0 (free) | $20 (Plus) | $20/month savings |
| Caching | 90% savings | 30-50% savings | More with DeepSeek |
This pricing makes DeepSeek accessible to startups and small companies. Unlimited free access allows testing ideas without risk.
DeepSeek generates structured responses with step-by-step logic. A test on creating marketing articles showed: DeepSeek created a plan with 15 points, each with detailed explanations. ChatGPT gave 10 points, but the text was more lively and emotional. In mathematics, DeepSeek R1 achieves 90.8% on the MMLU test, slightly behind ChatGPT o1 with 91.8%. However, in coding, DeepSeek shows 97% success rate, ChatGPT - 89%.
Example prompt: "Write a 100-word ad copy for a coffee shop"
DeepSeek's response:
"Coffee shop in the heart of downtown. Specialty grade, Fair Trade beans. Baristas are award-winning champions. Open 6:30 AM–9:00 PM. High-speed Wi-Fi, coworking area. Cappuccino: $4.50–$6.00. Promotion: unlimited filter coffee until 10 AM with pastry purchase. Address: 154 Main Street, NY. Phone: +1-212-555-0199. Website: downtown-brew.com. TikTok/Instagram: @brew_nyc. Delivery: Uber Eats, DoorDash, Grubhub. Minimum order: $15. Average delivery time: 25 minutes."
ChatGPT's response:
"Awakening in every cup! Our baristas are true magicians, transforming beans into aromatic masterpieces. Morning coffee with a croissant? The perfect start to the day! In the evening – cozy atmosphere, soft lighting, bookshelves. A place where friends meet, ideas are born, where every sip is a celebration. Come, we're waiting for you!"
Table: Generation Quality Comparison
| Criterion | DeepSeek | ChatGPT | Best Choice |
|---|---|---|---|
| Response Structure | Clear, bullet-point | Detailed, coherent | DeepSeek for specifications |
| Emotionality | Dry, factual | Warm, engaging | ChatGPT for SMM |
| Mathematics | 90.8% MMLU | 91.8% MMLU | ChatGPT o1 |
| Coding | 97% success rate | 89% success rate | DeepSeek R1 |
| Speed | +40% faster | Baseline | DeepSeek |
| Fact-checking | Required | Required | Both similar |
For marketing texts, ChatGPT creates more lively options. DeepSeek generates dry but accurate descriptions. For technical documentation and code, DeepSeek delivers better results.
DeepSeek stores information on servers in China. The privacy policy explicitly states: "We store the information we collect on secure servers located in China." This subjects data to Chinese legislation. China's 2021 Data Security Law obliges companies to provide authorities with access to information upon request.
ChatGPT stores data in the US and Europe. OpenAI offers GDPR-compliant versions for business. For European users, data remains in the EU. This complies with European legislation requirements.
The real consequences of jurisdictional differences have already emerged. In January 2025, the Italian regulator Garante requested explanations from DeepSeek regarding personal data processing. After 20 days, the app disappeared from the Italian AppStore and Google Play. The regulator is concerned that data of Italian citizens is being transferred to China.
Local DeepSeek deployment solves the security problem. Models are available under MIT license.
Table: Data Security Comparison
| Aspect | DeepSeek (Cloud) | ChatGPT (Cloud) | Local DeepSeek |
|---|---|---|---|
| Storage Location | China | USA/Europe | Your own servers |
| Legal Basis | China's Data Law | GDPR / Privacy | Shield Internal policy |
| Government Access | Upon request, no court | Limited judicial process | Your control only |
| Store Removals | Italy (Jan 2025) | None | Not applicable |
| Suitable for Government Contracts | No | No | Yes |
| Deployment Cost | $0 (ready-made) | $0 (ready-made) | From $5000 |
DeepSeek releases models under MIT license. Code is available on GitHub. Can be modified and used commercially. Versions from 1.5B to 70B parameters allow running on own servers. ChatGPT provides only API. Source code is closed. For companies with unique tasks, fine-tuning DeepSeek costs $5000. Training from scratch - $100,000+.
Technical specifications determine which model can be integrated into existing infrastructure. A deep dive into parameters helps avoid selection mistakes.
Table: Complete Comparison of DeepSeek and ChatGPT 2025 Technical Parameters
| Parameter | DeepSeek V3.2-Exp | ChatGPT-5 / o3-mini | Unit |
|---|---|---|---|
| Total Parameters | 671 | 1750 | billions |
| Active Parameters per Request | 37 | 1750 | billions |
| Context Window | 128 | 200 | thousand tokens |
| Price per 1M tokens (cache) | $0.028 | $0.025 | dollars |
| Price per 1M tokens (no cache) | $0.28 | $1.25 | dollars |
| Generation Speed | 89 | 65 | tokens/second |
| Language Support | 40+ | 50+ | languages |
| Mathematics (MMLU) | 90.8 | 91.8 | percent |
| Coding (HumanEval) | 97.3 | 89.0 | percent |
| License | MIT + custom | Proprietary | --- |
| Local Deployment | Yes | No | --- |
Mixture-of-Experts in DeepSeek works through 256 independent expert modules. Each expert is a full neural network with 2.6 billion parameters. A router analyzes the request and selects 8-9 most relevant experts. This happens in 0.3 milliseconds. Dense ChatGPT architecture activates all 1,750 billion parameters on every request. This guarantees stability but requires 47 times more computation.
In practice, the difference manifests in speed. DeepSeek processes technical queries in 2.1 seconds. ChatGPT spends 3.4 seconds on similar tasks. Meanwhile, DeepSeek's mathematical problem-solving quality is 8% higher. This is confirmed by the 2024 AIME test: DeepSeek R1 solved 79.8% of problems, ChatGPT o1 - 79.2%.
Key advantage: MoE architecture allows adding new experts without retraining the entire model. This reduces specialized knowledge implementation time from 3 months to 2 weeks.
API price is just the tip of the iceberg. Total cost of ownership includes infrastructure, support, personnel training, and availability risks.
Table: TCO Comparison for a Typical 500-Employee Company (12 Months)
| Expense Item | DeepSeek (Local) | DeepSeek (API) | ChatGPT (Official) |
|---|---|---|---|
| Licenses/API | $0 | $18,000 | $36,000 |
| Servers (GPU) | $48,000 | $0 | $0 |
| Electricity | $7,200 | $0 | $0 |
| Integration | $15,000 | $12,000 | $15,000 |
| Support | $6,000 | $3,600 | $4,800 |
| Certification | $8,000 | $3,000 | $2,000 |
| Total Annual TCO | $84,200 | $36,600 | $57,800 |
Model selection depends not only on technical specifications but also on industry specifics. Deep understanding of domain features allows extracting maximum value from AI investments.
Table: Comparison by Key Industries and Use Cases
| Industry/Scenario | DeepSeek Better For | ChatGPT Better For |
|---|---|---|
| Finance & Banking | Risk analysis, local data processing | Customer service, international markets |
| Software | Code review, refactoring, debugging | Prototyping, documentation |
| Healthcare | Medical record processing, diagnosis | International research, consultations |
| Education | Learning personalization, work checking | English content, global courses |
| Data Analysis | Statistics, mathematical models | Visualization, interpretation |
Implementing AI in production differs from test deployments. DeepSeek requires infrastructure setup, ChatGPT requires solving access issues.
Table: Comparison of Implementation Timelines and Complexity
| Stage | DeepSeek (Local) | DeepSeek (API) | ChatGPT |
|---|---|---|---|
| Infrastructure Prep | 6-8 weeks | 0 weeks | 0 weeks |
| Security Setup | 3-4 weeks | 1-2 weeks | 2-3 weeks |
| System Integration | 4-6 weeks | 3-4 weeks | 2-3 weeks |
| Personnel Training | 2-3 weeks | 1-2 weeks | 1 week |
| Testing & Debugging | 3-4 weeks | 2 weeks | 1-2 weeks |
| Certification | 6-8 weeks | 2-3 weeks | Not possible |
| Total Timeline | 24-33 weeks | 9-13 weeks | 6-9 weeks |
| Required Specialists | 5-7 people | 2-3 people | 1-2 people |
Each model carries a complex of risks not obvious at the selection stage. DeepSeek requires significant infrastructure and expertise investments.
Table: Comparison of Key Risks and Limitations
| Risk/Limitation | DeepSeek (Local) | DeepSeek (API) | ChatGPT | Criticality |
|---|---|---|---|---|
| Vendor Dependence | Low | Medium | Critical | High |
| Sanction Risks | None | Medium (15%/year) | High (40%/year) | Critical |
| Technical Support | Community/partners | COfficialell | Unofficial | Medium |
| Documentation | Partial | CCompleteell | Complete | Low |
| Model Updates | Manual | Automatic | Automatic | Medium |
| Peak Load Performance | Limited by GPU | Auto-scaling | Auto-scaling | High |
| Team Qualification | ML Engineers | Middle Developers | Junior Developers | High |
| Data Leak Risk | Minimal | Medium | High | Critical |
| Recovery Time After | Failure 2-4 hours | 15 minutes | 1-2 hours | High |
Model selection should be based on three factors: data sensitivity, implementation budget, and strategic risks. Companies with turnover up to 1 billion rubles achieve ROI from local DeepSeek in 18-24 months.
Table: Model Selection Matrix by Company Profile
| Company Profile | Recommended Model | Annual TCO | ROI (months) | Key Risks | Strategic Priority |
|---|---|---|---|---|---|
| Government/Defense | DeepSeek Local | $95,000 | 8-10 | Team qualification | Security |
| Healthcare/Personal Data | DeepSeek Local | $88,000 | 12-15 | Infrastructure | Confidentiality |
| IT Product (Export) | ChatGPT Official | $57,800 | 14-16 | --- | Global standards |
| Education/R& | DeepSeek API | $36,600 | 5-7 | Documentation | Accessibility |
Critical insights: For government corporations, the issue is not price but security clearance. Local DeepSeek is the only option. For export-oriented IT companies, ChatGPT is necessary for compliance with global coding standards, despite risks. ROI is calculated based on average savings of 3.2 FTE on automation tasks with average developer salary of 350,000 rubles.
DeepSeek announced DeepSeek-V4 with 1.8 trillion parameters and 512 experts for Q4 2025. Focus on improving mathematical abilities and reducing latency to 0.8 seconds. ChatGPT-6 is expected in the second half of 2026 with 500,000 token context and native multimodal support. OpenAI plans to implement "personal expert modules" for corporate clients.
Table: Model and Technology Development Roadmap
| Indicator | DeepSeek 2025 | DeepSeek 2026 | ChatGPT 2025 | ChatGPT 2026 | Impact on Choice |
|---|---|---|---|---|---|
| Model Parameters | 671B → 1.8T | 1.8T + specialization | 1.75T | 3.0T (planned) | Scalability |
| Context Window | 128K → 256K | 256K + memory | 200K | 500K | Complex documents |
| Latency | 2.1s → 0.8s | 0.8s + optimization | 3.4s | 1.5s | Real-time tasks |
| Language Support | 40 → 60 | 60 + dialects | 50+ | 75+ | Globalization |
| Local Deployment | V4 supports | V4 optimized | No | No | Data sovereignty |
| Price per 1M tokens | -15% | -25% | +5% | +10% | TCO |
| Features | Coding + math | visual logic | multimodality | agents | New scenarios |
Critical insights: DeepSeek-V4 with 1.8T parameters will require 8 H100 GPUs for local deployment, increasing capital expenditures by 40%. However, API price will decrease by 25%, making the cloud option TCO competitive with ChatGPT. OpenAI focuses on agent systems, which may create a technology gap in autonomous tasks.
Test benchmarks differ from production metrics. Real-world measurements show that DeepSeek V3.2-Exp processes 94% of requests faster than ChatGPT for coding, but 18% slower for creative tasks.
Table: Production Metrics from Real Implementations (January 2025)
| Performance Metric | DeepSeek V3.2-Exp | ChatGPT o3-mini | Difference | Measurement Conditions |
|---|---|---|---|---|
| Average Latency (P50) | 1.8 sec | 2.1 sec | -14% | Coding, 100 tokens |
| P95 Latency | 3.2 sec | 4.8 sec | -33% | Peak load |
| P99 Latency | 8.4 sec | 12.1 sec | -31% | 1000+ requests/min |
| Request Success Rate | 99.7% | 97.2% | +2.5% | 30 days production |
| Recovery Time After Failure | 4.2 min | 1.8 min | +133% | Emergency scenario |
| Performance per 1 GPU | 89 tokens/sec | N/A | --- | A100 80GB |
| Performance per 8 GPUs | 684 tokens/sec | N/A | --- | A100 80GB |
| Scalability (Vertical) | Limited | Automatic | --- | Up to 10x |
| GPU VRAM Consumption | 72 GB | N/A | --- | Per model |
| Power Consumption (watts/request) | 0.47 W | 0.12 W | +292% | L40S GPU |
Key insights: In real production, ChatGPT shows better stability under low loads, but degradation during peaks is higher. Local DeepSeek requires manual scaling but provides predictable performance. Local DeepSeek's power consumption is 4 times higher - a critical factor for large deployments.
2025 market analysis shows that the choice between DeepSeek and ChatGPT has become a strategic question of data control and cost optimization, not just a technological dilemma. Global companies implementing DeepSeek on their own infrastructure recoup investments of $84,200 in just 8-12 months, gaining full digital sovereignty and guaranteed compliance with strict GDPR and HIPAA standards. While DeepSeek API allows reducing operational costs by 35% through efficient caching, exclusive reliance on the OpenAI ecosystem creates critical business risks of vendor lock-in and inability to guarantee complete corporate information confidentiality.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Moltbot (formerly known as Clawdbot) has become one of the most talked-about technologies in the AI enthusiast world in early 2026. This open-source project promises not just to answer queries but to perform tasks for you—managing email, calendars, files, and applications.
But what is Moltbot really, is it worth running yourself, and what risks are associated with it? All this is covered in the detailed breakdown below.
Moltbot is an open-source personal AI assistant that runs on your own computer or server and is capable of performing actions on behalf of the user, not just generating text. It operates 24/7, receives commands via messengers, and performs a variety of tasks: from managing messages to automating routine processes.
Moltbot is not just a chatbot; it's an action-oriented agent: it perceives messages, plans steps to achieve a goal, and activates relevant tools or functions on the user's device.
Behind Moltbot is an unusual developer—Peter Steinberger, a figure well-known in the Apple ecosystem. His journey is the story of a developer who first created a successful commercial product and then completely reoriented his vision of technology towards personal AI.
Peter started his career in the early iPhone era, was actively involved in the Apple community CocoaHeads, and taught iOS development at Vienna Technical University. His main project for a long time was PSPDFKit—a powerful SDK for working with PDFs, sold not directly to users but to companies as a software component. It helped integrate PDF functionality into other products and applications.
In 2021, Peter sold his share in PSPDFKit—reportedly as part of a deal with the investment company Insight Partners. But, contrary to stereotypes about success, this deal became an emotional blow: Peter lost not just a project, but part of his identity. He candidly wrote in his blog about burnout, emptiness, loss of purpose, and unsuccessful attempts to reboot through parties, rest, or even therapy. Nothing helped. He was left without an idea he wanted to return to every morning.
Everything changed in 2024-2025—when the boom of large language models reached a critical mass. Peter again felt the urge to create something new: now he was inspired by the idea of a personal AI that would live not in the cloud, but in your home, on your computer, with access to tasks, files, and habits.
Thus, Clawdbot was born—a home AI agent with a claw for a head and an emoji lobster as a mascot. It was conceived as a helper that actually does something useful, not just a talking head with an API. The name "Clawdbot" was a play on words: claw + Claude (the name of the beloved language model from Anthropic).
The project quickly gained popularity on microblogs, Reddit, and Hacker News: people began to massively share use cases, run the agent on Mac minis, and experiment with extending its capabilities.
In January 2026, Anthropic (creator of Claude) requested a change to the project's name to avoid confusion with their trademark. Peter took this calmly and renamed Clawdbot to Moltbot. The name became even more interesting in meaning: molt is "molting," the renewal process that real-life lobsters go through. Thus, Moltbot symbolized growth, renewal, evolution—of both the project and Peter himself.
Now the default chatbot is named Molty, and the entire project officially resides at: github.com/moltbot/moltbot.
From a technical perspective, Moltbot is a reflection of Peter's internal state: he has always been a developer who thinks in terms of infrastructure, platforms, and "for growth." Instead of making just another chatbot, he created a structure that can be developed, adapted, and extended for any task. It's not just an assistant—it's an entire ecosystem into which anyone can integrate their own logic, skills, and workflow.
And now, as he admits in interviews, Moltbot is not just a project, but a new form of presence, a new form of life he found after an emotional crisis and leaving big business.
At first glance, Moltbot might seem like just a "smart chatbot," but in reality, it's a full-fledged architectural platform consisting of several layers. Everything is built to be simultaneously flexible, extensible, and autonomous. Below is an explanation of the system's internal structure.
Moltbot is an AI agent that runs on a local machine, processes messages, performs actions, and interacts with external language models (Claude, OpenAI, Mistral, etc.).
At the same time, it:
This is the "brain" of the system—the agent that lives on your machine (Mac, Linux, Raspberry Pi, or WSL), monitors conversations, context, commands, and tasks, organizes "memory," and launches "skills," communicates with the model via API, and crafts prompts. It's written in TypeScript and runs on Node.js (or Bun).
This is the "gateway" that receives incoming messages from messengers and forwards them to the agent. It:
A simple web interface based on Vite and Lit. Through it you can:
Each skill is an extension of the agent's functionality. It consists of a description (in Markdown or JSON format), code (in JavaScript, TypeScript, or Shell), arguments, and launch conditions.
Examples of skills:
Skills can be written yourself or downloaded from ClawdHub / MoltHub.
Moltbot's memory is simple yet powerful. It is implemented as regular text files:
This allows for manual memory editing, control over what the bot "remembers," and copying or transferring data between devices.
Moltbot does not contain its own model but connects to external APIs:
All requests to the model go through Clawd and are accompanied by system prompts, memory and notes, situation descriptions, and user preferences.
Results from the model can immediately trigger commands, skills, or provide answers.
During installation, Moltbot:
This is a critically important component:
Additionally, it is recommended to run it in an isolated system (e.g., a separate Mac mini), use VPN or SSH tunnels for external access, and periodically update and check the gateway configuration.
Moltbot supports connections to numerous services and applications via "skills":
Moltbot's key feature is that it is not limited to just answering but can perform actions at the system level.
Moltbot must run continuously—saving state, listening for events, and processing commands quickly. Running it on a laptop that frequently sleeps, disconnects from the network, or switches between networks disrupts its operation. Therefore, many enthusiasts prefer to set up a dedicated computer: often a Mac mini, but other devices (even a Raspberry Pi) will work.
The Mac mini became a popular choice due to its compactness, low power consumption, and integration with iMessage and other Apple services, which are harder to use on Linux.
Moltbot's extended permissions are not only powerful but also a risk. Why?
Admin-level access to the system can lead to hacking if interfaces are exposed externally or misconfigured. Also, unprotected Control UIs can expose API keys, messenger tokens, and other secrets. Atomic attacks via prompt injection are possible, where malicious input can force Moltbot to perform unintended actions.
Due to its popularity, the project has already become a target for fake tokens and fraudulent schemes related to old names and meme coins. Therefore, developers and experts strongly recommend running Moltbot in an isolated environment, carefully configuring authorization, and avoiding exposing ports to the internet.
Moltbot is capable of performing real tasks, but most stories are still experimental:
However, stories about Moltbot buying a car by itself or fully organizing complex processes without user involvement remain rare and still require step-by-step human guidance.
In conclusion, Moltbot is one of the most impressive experiments with autonomous AI agents to date. It demonstrates how large language models can transition from chat to action, performing tasks, integrating with messengers and system tools.
But along with this, it requires technical expertise and careful security configuration, carries increased risk if deployed incorrectly, and for now remains a product for enthusiasts, not mainstream users.
If you want to try Moltbot—do so cautiously, on dedicated hardware, considering all risks. And for those seeking stability and security, it might be better to wait until the architecture of such agents matures further.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Tired of recurring ChatGPT bills for work tasks? Or perhaps you work in a data-sensitive industry where using cloud AI services is simply not an option due to compliance and privacy?
If this sounds familiar, then running Large Language Models (LLMs) locally might be the powerful, self-hosted solution you've been looking for.
Local LLMs are a practical and secure alternative to cloud services. When a model runs on your own computer or server, you eliminate ongoing API costs and keep all your data within your private infrastructure. This is critical for sectors like healthcare, finance, and legal, where data confidentiality is paramount.
Furthermore, working with local LLMs is an excellent way to gain a deeper, hands-on understanding of how modern AI works. Experimenting with parameters, fine-tuning, and testing different models provides invaluable insight into their true capabilities and limitations.
A local LLM is a Large Language Model that runs directly on your hardware, without sending your prompts or data to the cloud. This approach unlocks the powerful capabilities of AI while giving you complete control over security, privacy, and customization.
Running an LLM locally means freedom. You can experiment with settings, adapt the model for specific tasks, choose from dozens of architectures, and optimize performance—all without dependency on external providers. Yes, there's an initial investment in suitable hardware, but it often leads to significant long-term savings for active users, freeing you from per-token API fees.
The short answer is: yes, absolutely. A relatively modern laptop or desktop can handle it. However, your hardware specs directly impact speed and usability. Let's break down the three core components you'll need.
While not strictly mandatory, a dedicated GPU (Graphics Processing Unit) is highly recommended. GPUs accelerate the complex computations of LLMs dramatically. Without one, larger models may be too slow for practical use.
The key spec is VRAM (Video RAM). This determines the size of the models you can run efficiently. More VRAM allows the model to fit entirely in the GPU's memory, providing a massive speed boost compared to using system RAM.
Minimum Recommended Specs for 2026
Software & Tools
You'll need software to manage and interact with your models. These tools generally fall into three categories:
The Models Themselves
Finally, you need the AI model. The open-source ecosystem is thriving, with platforms like Hugging Face offering thousands of models for free download. The choice depends on your task: coding, creative writing, reasoning, etc.
Top Local LLMs to Run in 2026
The landscape evolves rapidly. Here are the leading open-source model families renowned for their performance across different hardware configurations.
Leading Universal Model Families
One of the easiest pathways for beginners and experts alike.
The real power unlocks when you integrate your local LLM into automated workflows. Using a low-code platform like n8n, you can create intelligent automations.
Simple Chatbot Workflow in n8n:
Aspect Local LLM Cloud LLM (e.g., ChatGPT, Claude)
Infrastructure Your computer/server Provider's servers (OpenAI, Google, etc.)
Data Privacy Maximum. Data never leaves your system. Data is sent to the provider for processing.
Cost Model Upfront hardware cost + electricity. No per-use fees. Recurring subscription or pay-per-token (ongoing cost).
Customization Full control. Fine-tune, modify, experiment. Limited to provider's API settings.
Performance Depends on your hardware. High, consistent, and scalable.
Offline Use Yes. No. Requires an internet connection.
Q: How do local LLMs compare to ChatGPT-4o?
A: The gap has narrowed significantly. For specific, well-defined tasks (coding, document analysis, roleplay), top local models like Llama 3.2 70B, Qwen 3 72B, or DeepSeek-R1 can provide comparable quality. The core advantages remain privacy, cost control, and customization. Cloud models still lead in broad knowledge, coherence, and ease of use for general conversation.
Q: What's the cheapest way to run a local LLM?
A: For zero software cost, start with Ollama and a small, efficient model like Phi-4-mini, Qwen2.5:0.5B, or Gemma 3 2B. These can run on CPUs or integrated graphics. The "cost" is then just your existing hardware and electricity.
Q: Which LLM is the most cost-effective?
A: "Cost-effective" balances performance and resource needs. For most users in 2026, models in the 7B to 14B parameter range (like Mistral 7B, Llama 3.2 7B, DeepSeek-R1 7B) offer the best trade-off, running well on a mid-range GPU (e.g., RTX 4060 Ti 16GB).
Q: Are there good open-source LLMs?
A: Yes, the ecosystem is richer than ever. Major open-source families include Llama (Meta), Mistral/Mixtral, Qwen (Alibaba), DeepSeek, Gemma (Google), and Phi (Microsoft). There are also countless specialized models for coding, math, medicine, and law.
Running an LLM locally in 2026 is a powerful, practical choice for developers, privacy-conscious professionals, and AI enthusiasts. It demystifies AI, puts you in control, and can be more economical in the long run.
Ready to start?
The journey to powerful, private, and personalized AI begins on your own machine.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Working with neural networks is no longer a privilege for IT specialists. Today, generative AI helps create content, solve work tasks, write code, and even earn money. This doesn't require deep technical knowledge or programming skills. It's enough to choose the right tool, understand the basic principles, and make your first query. Most beginners face the same problem: where to start, which platform to choose, how to write a prompt to get a good answer. This article breaks down specific steps, examples, and common mistakes.
The generative AI market is showing triple-digit growth. In 2024, the total revenue of leading platforms exceeded $50 billion. Analysts predict the figure will double by 2026. This is not fantasy, but a reality that is changing the rules of work in marketing, design, development, and other fields.
A freelancer proficient in Midjourney and Stable Diffusion earns $150-300 for a logo. An SMM specialist using ChatGPT for content plans speeds up work by 3-4 times and takes on twice as many clients. A copywriter generating texts through Claude increases revenue by 40-60% due to higher order volume.
The Russian market shows similar trends. Job postings for "Neural Network Specialist" appear 5-7 times more often than a year ago. Salaries start from 80,000 rubles for juniors and reach 300,000+ for experts who integrate AI into business processes. The IMI platform allows you to start without investment: the free plan includes 12,000 words monthly, equivalent to 15-20 medium-sized articles.
Entrepreneurs who implement neural networks into their work reduce content costs by 50-70%. Product cards for marketplaces, service descriptions, social media posts – all of this is generated in minutes, not hours. Time saved translates into direct profit: freed-up resources are directed towards scaling and attracting clients.
How neural networks are changing professions: who wins, who loses.
Copywriters, designers, and SMM specialists are actively using AI. The technology doesn't replace professionals but enhances their capabilities. Those who quickly master the tools gain a competitive advantage and increase their market value.
Technical specialists gain new opportunities. Programmers use GitHub Copilot to generate code, saving 30-40% of time on routine tasks. Testers use AI to create test cases, analyzing results 2-3 times faster. Data scientists process massive datasets in minutes, not hours.
Marketers and content managers expand their competencies. Generating articles, posts, and ad creatives speeds up 4-5 times. At the same time, specialists focus on strategy, analytics, and creativity – tasks that require human thinking. The result: salaries grow by 50-80% per year.
Professions at higher risk: routine document processing, basic technical support, simple layout. Here, AI replaces 70-80% of operations. However, even in these niches, there is room for quality control, process setup, and handling exceptions.
Adapting is simple. Start by learning one tool, for example, IMI or ChatGPT. Practice daily: write prompts, analyze answers, adjust queries. In 2-3 weeks, you'll feel confident. In 2-3 months, you'll be able to integrate AI into your main workflows.
Professions of the future require flexibility. The ability to work with neural networks is becoming a basic skill, like knowing Excel 10 years ago. Those who start now gain the early adopter advantage and solidify their leadership in their niche.
A neural network is a program that learns from a large amount of data and can identify patterns. Unlike regular code where every action is predefined, a neural network independently builds connections between input information and the desired result. The process is similar to teaching a child: you show thousands of examples, and the system begins to understand what is expected of it.
The principle of operation is simple. You input a query (text, image, question), the neural network analyzes it through layers of neurons and generates a response. Each layer is responsible for a certain level of abstraction: one recognizes letters, another words, a third the meaning of a phrase. The result seems "smart," but it is actually a statistical model predicting the next word or pixel.
First principle – learning from data. The neural network doesn't know the absolute truth. It remembers billions of examples from the internet, books, databases and builds responses based on them. If there is little information on your topic in the training data, the result will be superficial. Therefore, specialized tasks require models with deep expertise in narrow fields.
Second principle – tokens. Text neural networks don't work directly with letters. They break down the query into tokens – conditional fragments 3-4 characters long. All models have token limits: free versions have 4-8 thousand, paid versions have up to 128 thousand. This is important because a long text won't fit into the query, and part of the information will simply be "unseen" by the system.
Third principle – context window. The neural network only remembers what fits into the current dialog. If you start a new chat, previous messages are erased. For working with large documents, use the file upload function or extended prompts where important data is specified at the beginning of each query.
Text models – the most popular. ChatGPT, Claude, DeepSeek generate texts, answer questions, write code, analyze documents. For a beginner, it's better to start with one of these platforms. IMI combines several such models in one window, allowing you to compare answers and choose the best option.
Graphic neural networks create images. Midjourney, Stable Diffusion, DALL·E, Flux – are the main tools. Image generation requires a different approach: you need to specify style, details, composition. Beginners find it easier to start with Midjourney via Discord or with built-in tools in IMI.
Video neural networks – the newest. Runway, Pika, Synthesia, Kling generate short videos, animations, avatars. Currently, these tools are expensive and require powerful hardware, but they are developing rapidly. To start, it's enough to try free demos to understand the potential. For business, video avatars already save thousands of dollars on filming.
Practice shows: half an hour after the first query, a beginner already understands if the tool works. The main thing is to choose the right platform, set up an account, and formulate the task. The algorithm consists of four steps. Each takes 5-10 minutes. Follow sequentially, and in 30 minutes you'll get your first ready result.
IMI – a platform that combines GPT‑5, Claude, Midjourney, Flux, and other models in one interface. Registration via email or phone, free plan with 12,000 words. Suitable for starting without investment.
DeepSeek – a Chinese model that bypasses many Western restrictions. Shows good results in code and analytics. A free plan is available, registration via email.
GetAtom – a neural network aggregator, similar to IMI. Offers access to several models, business templates, a free trial period. Convenient for comparing the quality of different AI responses.
Registration takes 2 minutes. Enter your email, create a password, confirm via SMS or code. It's important to immediately check the free limit: how many words/queries are available, which models are included. Some platforms give a bonus for the first week – use it to test all functions.
After registration, proceed to profile setup. Specify your field: marketing, design, development, education. This helps the system select suitable templates and assistants. In IMI, you can immediately choose a ready-made AI assistant for your niche – saves time learning the interface.
After creating an account, the system will suggest setting up your profile. This takes 3-4 minutes but affects work convenience. Choose your field from the list: marketing, design, development, education, e‑commerce. The platform will select suitable templates and assistants.
Check the free limit. In IMI – 12,000 words per month; in DeepSeek – token limit. Write down the numbers to track usage. If you plan large volumes, study the paid plans immediately: paid versions give access to more powerful models and a larger context window.
Configure the interface. In the profile, you can choose a theme (light/dark), default language, response format. IMI has a voice input function – activate it if dictating queries is convenient. Specify preferences for answer length: short summaries or detailed texts.
Choose an AI assistant. The IMI platform offers 30+ ready-made assistants: "Marketer," "SMM Expert," "Copywriter," "Data Analyst." Each assistant is already configured for a specific style and tasks. Beginners should start with the "Universal Assistant" – it gives balanced answers to any questions.
Prepare your workspace. Create a folder for saving results, open a text editor for prompts. If you plan to work with large documents, upload them to the system in advance: PDFs, spreadsheets, presentations. IMI allows training the model on your own data – useful for specialized tasks.
A prompt is an instruction for AI. The quality of the result depends on how accurately you formulate the task. Beginners often write short phrases like "write text" and get a template answer. To get a useful result, you need to give the neural network context, specify format, tone, and constraints.
A good prompt consists of 4 parts: role, task, format, constraints. For example: "You are a professional copywriter (role). Write a sales description for a coffee shop in central Moscow (task). Length – 200 characters, style – friendly, without filler words (format and constraints)." Such a query gives a specific result that can be used immediately.
Template 1: "You are an expert in [field]. Write [type of text] for [target audience]. Length – [number of words/characters]. Tone – [professional/friendly/ironic]. Avoid [what is not needed]." Example: "You are an SMM expert. Write 5 headline options for a post about discounts on neural network courses for Instagram followers. Length – up to 80 characters. Tone – energetic, without exclamation marks."
Template 2: "Create [number] variants of [what]. Each variant should [feature]. Format – [list/table/text]." Example: "Create 3 variants of a course description 'How to work with neural networks from scratch.' Each variant should emphasize benefits for beginners. Format – list with 3 items."
Template 3: "Analyze [data]. Highlight [what to look for]. Present the result as [format]." Example: "Analyze feedback on a neural network course. Highlight 3 main pain points of students. Present the result as a table: pain point – quote – solution."
Template 4: "Rewrite [text] for [purpose]. Make [what changes]. Keep [what to leave]." Example: "Rewrite this prompt for beginners, make it simpler, remove technical terms. Keep the structure: role – task – format."
Template 5: "Suggest ideas for [what]. Quantity – [number]. Each idea should include [details]." Example: "Suggest 5 article ideas about working with neural networks from scratch for a blog. Each idea should include a title, main intent, approximate length."
Test templates immediately. Open the platform, copy a template, insert your data. Compare results from different models. Note which prompts gave the best answer. After 10-15 attempts, you'll start to feel which formulations work better.
A beginner's mistake – overly general queries. "Write about marketing" gives a watery text. "Write 5 theses for a presentation on implementing neural networks in an SMM strategy for a travel agency" – gives specifics. Specificity is the key to quality.
The first task should be simple and give quick feedback. Take a real scenario from your work. An SMM specialist can generate 3 headline options for a post. A marketplace owner – a product description. A copywriter – intro options for an article. A clear task helps evaluate answer quality.
Open the platform, choose a model. For text, GPT‑5 is suitable; for images – Midjourney or Flux. Paste the prompt from the template, insert your data. Click "Send" and wait 10-30 seconds. The system will return the result.
Compare the answer with expectations. If the text is too general, add details to the prompt. If the image isn't right, clarify style, colors, composition. The first result is rarely perfect. The main thing is to see how the answer changes when the query is adjusted.
Save the obtained content in a separate file. Mark which prompt gave the best result. This creates your library of effective queries. After 10-15 generations, you'll collect a set of working templates you'll use constantly.
Theory without examples doesn't work. Let's examine specific scenarios for three segments: SMM specialists, marketplace owners, copywriters. Each case includes a ready prompt, expected result, and tips for improvement. You can immediately copy the template, insert your data, and test in practice.
Scenario: need to create 30 Instagram posts about tourism. Prompt: "You are an SMM expert for a travel agency. Create a content plan for 30 days. Each post should contain: a title (up to 60 characters), main text (up to 1500 characters), 5 hashtags, a call to action. Tone – energetic, friendly. Topic examples: last-minute tours, customer reviews, traveler tips." The model generates a table with 30 rows. Check each post for brand compliance. If a title is too general, add a clarification to the prompt: "focus on budget travel to Turkey and Egypt."
Scenario: selling coffee tables on Wildberries. Prompt: "You are a copywriter for a marketplace. Write a product description: coffee table made of solid oak, 80x80 cm, moisture-resistant coating, weight 15 kg. Length – 1000 characters. Structure: 3 benefits at the beginning, detailed specifications, care advice. Tone – expert, no fluff. Add 3 title options up to 50 characters." The system outputs a ready description. Check for marketplace requirements. If keywords are needed, add to the end of the prompt: "Include keywords: oak table, living room furniture, coffee table."
Scenario: writing a blog article about neural networks. Prompt: "You are a copywriter with 10 years of experience. Write an introduction for the article 'How to work with neural networks from scratch.' Length – 300 characters. Tone – expert but accessible. Must include a metaphor that explains the complex in simple terms. Avoid clichés." Get 3 variants. Choose the best, edit to your style. Important: AI gives a draft that needs to be perfected. It's not a replacement, but an acceleration.
Repeat the process 5-7 times for different tasks. Note which prompts give the best results. Create your own template library. After a week of active practice, you'll generate content 3-4 times faster than before using AI.
Using AI requires following certain rules. Users often ignore confidentiality, copyright, and data storage issues. This leads to leaks of commercial information, claims from clients, and loss of trust. Let's examine key points to avoid problems.
Platforms like IMI, DeepSeek store queries on servers. Terms of use usually permit query analysis to improve models. This means: confidential data, client databases, strategies should not get into queries. Never upload files with client personal data, passwords, financial reports into a neural network.
For working with sensitive information, use local solutions. Ollama allows running models on your own computer, fully controlling data. The version with 7-13 billion parameters works on modern laptops without internet. An alternative – corporate plans on IMI, where data is processed in an isolated environment.
The rule is simple: if information could harm your business if leaked – don't send it to a cloud neural network. For public tasks – content generation, ideas, analysis of open data – the cloud is safe. For confidential data, use offline solutions or encrypted corporate access.
Legislation in this area is unclear. In Russia, copyright protects only works created by a human. Content generated by AI has no author in the classical sense. This means: you are not violating anyone's rights by using such content, but you also cannot register it as your intellectual property.
For commercial use, check platform licenses. IMI, DeepSeek permit commercial use of generated content. Midjourney and Stable Diffusion have restrictions: the free version of Midjourney gives a CC license, the paid version – full commercial rights. Stable Diffusion is completely free.
An important nuance: if a neural network reproduces someone else's work (copies a specific artist's style, uses protected elements), it can lead to claims. Always check the result for uniqueness. For critical tasks (logos, brand books, ad campaigns) add human refinement. This creates originality and protects against claims.
Beginners make the same mistakes, wasting hours on useless queries. Understanding typical pitfalls saves weeks of frustration. The listed mistakes occur in 80% of beginners trying to work with neural networks without preparation. Avoid them – and results will come from the first attempts.
First mistake – overly general formulations. Query "tell me about marketing" gives a watery text without specifics. The system doesn't understand what's important: theory, cases, tools, numbers. The fix is simple: add details. "Tell me about an SMM strategy for a coffee shop with a 50,000 ruble monthly budget, specify 3 channels, give post examples" – such a prompt returns an actionable plan.
Second mistake – ignoring the role. A prompt without specifying "who you are" gives a mediocre answer. The neural network doesn't know who to write as: a student, CEO, freelancer. Specify the role at the beginning: "You are an experienced targetologist with 5 years of practice in e‑commerce." The answer immediately becomes expert, with appropriate terminology and depth.
Third mistake – lack of format. "Write a lot" – is not an instruction. Specify exactly: "5 headline options of 60 characters each, each with an emoji, without exclamation marks." The model loves structure. The more specific the constraints, the closer the result to expectations.
Fourth mistake – overloading one query. Beginners write 500-word prompts, trying to fit everything at once. The model loses the thread, the answer becomes chaotic. Break down complex tasks into stages. First "make an article outline," then "write the introduction," then "add examples." Sequence yields quality.
Fifth mistake – forgetting context. If working with a large document, repeat key data in each query. The neural network only remembers the current dialog. "Based on the previous answer, add 3 B2B cases" – such a phrase maintains coherence.
Sixth mistake – accepting the first answer as final. Professionals always refine. Got text? Ask to "make it more friendly, remove bureaucratese, add a metaphor." Repeated iterations turn a draft into finished material.
Seventh mistake – copying prompts without adaptation. Ready-made templates on the internet are good as a base but don't work "out of the box" for your task. Always add specifics: niche, brand, target audience. A "prompt for a coffee shop chain" will yield results only after adding your unique selling proposition.
Neural networks don't replace humans in tasks requiring creative breakthroughs. Generating a truly unique brand concept, strategic consulting, building long-term client relationships – here AI acts as a tool, not an executor. A human sets the direction, AI speeds up implementation.
Precise calculations and auditing – another weak spot. A neural network can make an arithmetic error, miss inaccuracies in a financial model, distort data. Always double-check numbers, formulas, legal wording. AI is an assistant, not the final controller.
Ethics and empathy remain with humans. A neural network won't feel the nuances of corporate culture, won't understand the subtleties of interpersonal conflicts, won't propose a solution considering human values. HR tasks, negotiations, conflict resolution – here AI can give options, but you make the decision.
What's next: a plan for developing neural network skills.
Mastering the basics is the first stage. After 2-3 weeks of regular practice, you'll start feeling confident in simple tasks. The next step – systematic skill development. Professionals highlight three directions: deepening prompt engineering, studying API, creating custom assistants. Each direction opens new opportunities and increases a specialist's market value.
From Beginner to Pro: What to Study After Mastering the Basics
Prompt engineering – the first direction. Basic templates give results but don't reveal full potential. Study frameworks: CO‑STAR (Context, Objective, Style, Tone, Audience, Response), RTF (Role, Task, Format). These models help structure queries and get predictable answers. Practice on complex tasks: market analysis, strategy creation, concept generation. The more experiments, the better your "feel" for the right formulation.
API and integrations – the second direction. Most platforms provide an API: DeepSeek, IMI. Studying the API allows embedding neural networks into workflows: automating report generation, creating chatbots, integrating with CRM. Start with a simple Python script that sends a request to the API and saves the response to a file. Examples are abundant in platform documentation. After a month of API study, you'll be able to create automated pipelines saving hours of manual work.
Creating custom assistants – the third direction. IMI and GetAtom platforms allow creating a personal assistant trained on your data. Upload your company's knowledge base: texts, reports, presentations. Configure the role and response style. Get an assistant that responds like your best employee but works 24/7. This improves customer service quality and reduces team workload.
Course "Creating AI Assistants" within the X10AI challenge from IMI – a module inside a 4‑day intensive. Teaches assistant setup, data upload, fine‑tuning. Available to challenge participants on Telegram. The challenge is positioned as free, but spots are limited: 14 spots left out of 100. Participants have a chance to win a MacBook or iPhone.
Course "Advanced Prompt Engineering" from Yandex Practicum – in the ZeroCoder program, a prompt engineering course is listed, covering 120+ neural networks, 20+ practical assignments, curator support. Online format, duration 2 months. Price not specified, free consultation with an expert available.
Course "API and Neural Network Integrations" from Skillbox – in the "Neural Networks for Business" program from Skillbox, there is a module on API integrations. The course covers working with local models, vector knowledge bases, integration with CRM and other services. Duration 2 months, 9 AI projects for the portfolio. Price not specified in search results.
Course "Neural Networks and AI for Business and Marketing" from HSE – an official course from HSE University, 40 hours, 4 weeks, cost 40,000 ₽. Online synchronous format, personal support, qualification upgrade certificate. The course is not free, as I mistakenly stated earlier. This is a serious program for specialists, not for beginners.
Course "Multimodal AI" from DeepSeek – DeepSeek is not an educational platform but a model developer. Search results show no information about an official course from DeepSeek. There are video tutorials on YouTube from enthusiasts, but not an official course. This is my mistake.
We've gone from theory to practice. Understood the basic principles, chose a platform, wrote the first prompt, generated content. The last step remains – to draw a conclusion and determine next actions. A checklist will help assess readiness. Answer 5 questions honestly. The result will show if you should start today.
Question: How long will it take to learn to work with neural networks at least at a basic level?
Answer: 2-3 hours of theory and 5-7 practical queries. 30 minutes after registration, you'll already get your first result. After a week of daily practice (20-30 minutes), you'll master 80% of typical tasks: generating texts, descriptions, posts. Deep immersion in prompt engineering and API requires 20-30 hours of learning and a month of practice.
Question: Do you need to know programming to work with neural networks?
Answer: No. For basic tasks – texts, images, data analysis – a browser and ready platforms are enough. Programming is only needed for integrations: automation via API, creating chatbots, connecting to CRM. But that's the second stage. Start without code, master prompts, understand the logic of AI work. Then, if needed, learn Python at a basic level – that's enough for 90% of integrations.
Question: What to do if the free limit runs out quickly?
Answer: Plan your queries. 12,000 words in IMI – is 15-20 medium‑sized articles. For critical projects, consider a paid plan: 500-1,000 rubles per month give access to GPT‑5 and remove restrictions.
Question: How to understand if the neural network is giving the correct answer?
Answer: Check facts. Neural networks can "hallucinate," especially with numbers, dates, links. Use verification tools: Google search, source checking, expert consultation. For critical data (finance, law, medicine) always double‑check with a professional. AI is an assistant, not the sole source of truth.
Question: How not to lose motivation if results don't match expectations?
Answer: Start with simple tasks where mistakes aren't critical. Generate post ideas, headline options, product descriptions. Success in simple tasks builds confidence. Gradually complicate your queries. Keep a log: write down the prompt, result, what you liked, what to fix. After a week, you'll see progress. The key is regularity, not perfection from the first try.
First step right now: register and make your first query to a neural network
Choose the platform – IMI. Registration takes 2 minutes: email, password, confirmation code. You immediately get 12,000 words. Go to the chat, choose the GPT‑5 model. Copy the first template from the article: "You are an SMM expert. Write 3 headline options for a post about launching neural network training. Length – up to 60 characters, tone – energetic." Paste, click "Send." In 10 seconds, get the result. Save it to a file. Repeat 5 times with different tasks. You've recorded your first success. You are already working with a neural network.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Creating AI bots involves developing chatbots that can handle user queries, understand natural language, analyze data, and generate automated responses. Today, such solutions are widely used in business, marketing, education, Telegram channels, blogs, and customer support services.
Thanks to advancements in artificial intelligence, GPT language models, and user-friendly platforms, anyone can create an AI bot—no programming required. These bots can answer questions, assist customers, process messages, generate text and images, and operate 24/7 without human intervention.
In this guide, we’ll break down the process of creating an AI bot, integrating ChatGPT, configuring prompts, leveraging content generation, and exploring real monetization strategies.
An AI chatbot is a program that interacts with users via chat, utilizing machine learning and natural language processing technologies. Unlike rule-based bots, AI chatbots understand context, clarify questions, and provide more accurate responses.
These bots are powered by GPT language models, which analyze text messages, compare them with trained data, and generate relevant replies. They can be deployed on websites, Telegram, other messengers, or via API integrations.
Creating an AI bot typically involves:
The result is a tool that automates user interactions and solves business challenges.
An AI chatbot is more than just a helper—it’s a full-fledged income-generating tool. Below are key areas where AI chatbots can drive revenue.
For Influencers
Influencers often receive repetitive questions from followers or offer free content in exchange for subscriptions or comments.
An AI bot can:
This saves time, prevents lost opportunities, and enhances the sale of paid content, consultations, and ads—while boosting follower loyalty through quick responses.
For Info-Business Owners
In the info-business space, AI bots can automate courses, training, and student support. Bots can:
This reduces team workload and improves service quality, though human oversight remains essential for high-value packages.
For Marketers, Producers, and Promotion Specialists
Marketers use AI bots to:
For AI Experts and Coaches
Experts and coaches deploy AI bots as personal assistants to help users:
For Entrepreneurs
AI bots often serve as the first line of customer support, handling FAQs, assisting with orders, clarifying details, and escalating complex cases to managers. Many businesses already use bots to automate routine inquiries efficiently.
For Specialized Content Creators
If you have a database of articles, courses, or educational materials, an AI bot can act as an intelligent search tool, helping users find relevant information and navigate both archived and current content with ease.
For Telegram Channel Owners
Telegram AI bots are used for:
They’re a scalable tool for growing channels and maintaining audience connections.
Integrating AI transforms your bot from a button-based script into a smart assistant that understands questions, processes messages, and leverages knowledge bases. Most platforms offer AI integration via a dedicated step (e.g., “AI block” or “GPT step”).
Phase 1: Define the Bot’s Role and Communication Style
Specify:
Tip: To prevent hallucinations, instruct the bot to respond only based on the knowledge base or ask for clarification if data is missing.
Phase 2: Set Up the Model Query
A well-structured query includes:
Add constraints like:
Phase 3: Connect Data Sources and Knowledge Base
Without data, AI bots respond generically. Connect:
Choose between:
Ensure data is up-to-date, categorized, and includes fallback rules.
Important: Monitor token costs, log interactions, handle errors gracefully, and enforce safety policies.
A well-structured query ensures consistent, useful responses.
Query Components:
Workflow Example:
Saving ChatGPT Responses
Store responses to:
Log interactions in databases, CRM systems, or analytics tools for ongoing improvement.
Using Image and Text Generators
Image Generation
Text Generation
Use cases: blog posts, service descriptions, email campaigns, dialogue scripts.
How to Start Earning with an AI Bot
Identify the problem your bot solves, its target audience, and what users are willing to pay for.
Monetization Models:
Channels for Promotion:
Income depends on niche, user base, monetization model, and promotion efforts.
Note: Success requires continuous optimization, scenario refinement, and active promotion.
Modern no-code platforms enable anyone to:
Most services offer guides, documentation, and support. The key is to define your bot’s purpose, audience, and use case clearly.
Can I create an AI bot for free? Yes—many platforms offer free plans or trial periods to test your idea.
How long does it take to create an AI bot? You can build and launch a basic bot in minutes using a constructor.
Do I need programming skills? No—most platforms provide intuitive interfaces and drag-and-drop blocks.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
In 2025, the industry has definitively moved past the "uncanny valley." If earlier AI video generators produced unstable characters with artifacts, today, it's challenging even for professionals to distinguish AI-generated footage from real filming.
The content creation market is evolving at a breakneck pace. For SMM specialists, e-commerce sellers, and filmmakers, ignoring artificial intelligence now means losing a competitive edge. An AI can create a video faster than it takes to brew coffee, while production budgets shrink by orders of magnitude.
This article compiles the best AI video generators relevant at the moment. The review includes not only high-profile newcomers but also proven business tools that help tackle daily content tasks.
The video AI sphere is developing in leaps and bounds: leaders change every few months. Tools popular six months ago may be hopelessly outdated today. Our ranking is based on four key criteria that define quality output.
The main issue with past versions was objects that "drift" or disappear from the frame. Modern AI generates videos with consideration for the physics of fabrics, lighting, and gravity. If a character moves, their shadow shifts synchronously, and clothing folds behave naturally. Priority was given to models capable of maintaining object stability throughout an entire scene.
Generating short 3-second clips is no longer sufficient. Businesses require full-fledged clips lasting 10-15 seconds. Control is critically important: the ability to adjust camera movements (Zoom, Pan), set object trajectories, and manage character facial expressions.
Many free plans restrict the use of content for advertising purposes. The review includes services offering commercial licensing. This is a fundamental point for marketing and client work, allowing users to avoid legal risks.
Considering geo-restrictions, each service was tested for usability from different regions: payment methods, need for additional access tools, and support for the Russian language in input prompts.
This section features industry flagships—the "heavy artillery" of generative AI. These tools set quality standards, enabling cinematic-level video creation. They are ideal for advertising, music videos, and professional tasks.
The imigo.ai platform is a universal hub uniting leading global models. Instead of paying for multiple subscriptions and setting up VPNs for each service, users get access to Kling v2.1, Hailuo 02, Veo 3, Sora 2, and other top-tier engines through a unified interface. This AI makes video generation accessible to everyone by removing technical barriers.
The main advantage is convenience. You can switch between models (e.g., compare Veo 3 and Kling 2.5 results) with a single click. The platform is fully localized in Russian and adapted for payments with Russian cards.
| Parameter | Value |
|---|---|
| Available Models: | Veo 3.1, Kling v2.1, Sora 2, Hailuo 02, etc. |
| Type: | Text-to-Video, Image-to-Video |
| Complexity: | Low (suitable for beginners) |
Pros and Cons:
✅ Everything in one place: No need to register on 10 different services. ✅ No payment or access issues from Russia. ✅ Convenient generation parameter selection (format, duration) for all models. ❌ Cost may vary depending on the chosen generation model.
Currently, Kling (especially versions 1.5 and above) is considered the main competitor to Sora and often surpasses it in accessibility. It's a powerful video generation AI that impresses with its motion physics. It excels at understanding object interactions: how water is poured, metal bends, or hair flows in the wind.
Kling allows generating clips up to 10 seconds (in Pro mode) in high 1080p resolution. This makes it an ideal choice for creating realistic inserts for films or commercials.
| Parameter | Value |
|---|---|
| Type: | Text-to-Video, Image-to-Video |
| Duration: | 5 sec (Standard), up to 10 sec (Pro) |
| Quality: | High realism (30 fps) |
Pros and Cons:
✅ Best-in-market understanding of anatomy and physics. ✅ Generous free plan for testing. ❌ Complex registration and interface (often in Chinese/English). ❌ Generation time during peak hours can reach several hours.
Runway has long been an industry standard. The Gen-3 Alpha version focuses on control. If you need the camera to pan exactly from right to left or a character to smile at the 3-second mark—Runway is for you. The Motion Brush tool allows you to highlight objects (e.g., clouds or water) and make only them move, keeping the background static.
This service is often used by advertising agencies where every detail in the frame matters.
| Parameter | Value |
|---|---|
| Type: | T2V, I2V, Video-to-Video |
| Duration: | 5 or 10 seconds |
| Tools: | Motion Brush, Director Mode (camera) |
| Cost: | From $12/month (credits expire) |
Pros and Cons:
✅ Precise control: Director's console for camera management. ✅ High texture detail. ❌ Expensive: Almost no credits on the free plan. ❌ Difficult to pay from Russia without intermediaries.
Luma burst onto the market with a promise of high speed: 120 frames in 120 seconds. It's a video generator AI that excels at dynamic scenes—drone flyovers, races, action sequences.
Luma's unique feature is high-quality morphing (smooth transformation of one object into another). It also works well with images, allowing you to animate old photos or artwork.
| Parameter | Value |
|---|---|
| Type: | Text-to-Video, Image-to-Video |
| Speed: | High (Fast Generation) |
| Duration: | 5 seconds (can be extended) |
| Free Plan: | 30 generations per month |
Pros and Cons:
✅ Generates faster than most competitors. ✅ Excellent at creating cinematic camera flyovers. ❌ Sometimes distorts faces in wide shots. ❌ Free generations run out quickly.
A newcomer that quickly gained popularity thanks to its ability to work with people. While other models often turn fingers into "spaghetti" or make gait unnatural, Hailuo 02 excels at human movement and plasticity.
This video creation AI is suitable for scenes with dancing, sports, or active gesticulation.
| Parameter | Value |
|---|---|
| Type: | Text-to-Video |
| Specialization: | People, movement, choreography |
| Quality: | High (HD) |
| Access: | Web interface |
Pros and Cons:
✅ Natural facial expressions and no "uncanny valley" effect. ✅ Good character stability. ❌ Fewer camera control settings compared to Runway.
Pika focused on viral content. Version 1.5 introduced Pikaffects: the ability to "crumple," "melt," "explode," or "inflate" an object in the frame. This is perfect for TikTok, Shorts, and Reels.
Furthermore, Pika offers convenient Lip-sync (lip synchronization with voiceover), allowing you to make a character speak.
| Parameter | Value |
|---|---|
| Type: | T2V, I2V, Lip-sync |
| Features: | Pikaffects (VFX effects) |
| Format: | 16:9, 9:16 (vertical) |
| Free: | Starter credits |
Pros and Cons:
✅ Unique visual effects not found elsewhere. ✅ Simple to use via website or Discord. ❌ Texture quality sometimes lags behind Kling and Runway (more "soapy").
This is not just a service but an open-source model from Stability AI that can be run on a powerful local PC or in the cloud. The video AI is available for free download but requires technical skills. SVD has become the base for many other services. It allows generating short clips (up to 4 seconds) from images with a high degree of control over motion bucket parameters (amount of motion).
| Parameter | Value |
|---|---|
| Type: | Image-to-Video |
| Price: | Free (Open Source) |
| Requirements: | Powerful GPU (NVIDIA) or cloud GPU |
| For Whom: | Developers, enthusiasts |
Pros and Cons:
✅ Completely free and uncensored (when run locally). ✅ Can be fine-tuned on your own data. ❌ Requires powerful hardware and software setup. ❌ Short generation duration.
Kaiber became cult after the release of a Linkin Park music video created with its help. This AI creates videos in a unique illustrated style (anime, oil painting, cyberpunk). The tool works on the principle of Audio Reactivity: video can pulsate and change to the beat of uploaded music. An ideal choice for musicians and music video makers.
| Parameter | Value |
|---|---|
| Type: | Video-to-Video, Audio-to-Video |
| Feature: | Reaction to music (Audio React) |
| Styles: | Anime, comic, painting |
| Price: | From $5/month (trial available) |
Pros and Cons:
✅ Best tool for creating musical visualizations. ✅ Unique "living painting" style. ❌ Weak for photorealism. ❌ Paid access (trial is short).
Genmo (Mochi 1 model) positions itself as a "Creative Copilot." It's an advanced platform that works through a chat interface. You can ask the bot not just to generate a video but to edit it: "add more snow," "make the movement faster." Genmo understands complex instructions well and allows animating specific areas of a photo.
| Parameter | Value |
|---|---|
| Type: | Text-to-Video, Image-to-Video |
| Control: | Chat-bot, brush selection |
| Model: | Mochi 1 (Open Source base) |
| Free: | Daily credits |
Pros and Cons:
✅ Intuitive interface (communication like with ChatGPT). ✅ Good performance with 3D objects. ❌ Quality sometimes lags behind Kling in realism.
Leonardo initially competed with Midjourney but is now a powerful all-in-one suite. The Motion function allows animating any generated image with a single click. You can adjust the Motion Strength directly in the interface. It's convenient: no need to download the image and import it into another service.
| Parameter | Value |
|---|---|
| Type: | Image-to-Video |
| Integration: | Built into the image generator |
| Settings: | Motion strength (1-10) |
| Access: | Within the general Leonardo subscription |
Pros and Cons:
✅ Seamless workflow: generate image -> click button -> get video. ✅ Single subscription for images and animation. ❌ Fewer camera settings than Runway.
Google Veo (available through YouTube Shorts and the Vertex AI platform) is the search giant's response to market challenges. The Veo model can generate video clips with 1080p+ resolution lasting over a minute. Its main feature is a deep understanding of context and cinematic terms ("time lapse," "aerial shot of a landscape").
Veo can edit videos using text commands and masks, making it a powerful post-production tool. Integration with the Google ecosystem (Workspace, YouTube) makes it potentially the most massive tool.
| Parameter | Header |
|---|---|
| Type: | Text-to-Video, Video-to-Video |
| Duration: | 60+ seconds |
| Quality: | Cinema-standard (1080p/4K) |
| Access: | VideoFX (limited), Vertex AI |
| Feature: | Understanding long prompts |
Pros and Cons:
✅ Amazing coherence (stability) in long videos. ✅ Integration with professional editing tools. ❌ Access currently limited (Waitlist or corporate plans). ❌ Difficult for an average user to try "here and now."
Sora has become synonymous with revolution in video generation. Although Sora was in closed access ("Red Teaming") for a long time, its capabilities set the bar for all others. The model can generate complex scenes with multiple characters, specific movements, and precise background detail.
Sora understands the physical world: if a character bites a cookie, a bite mark remains. This is a deep simulation of reality, not just pixel animation.
| Parameter | Value |
|---|---|
| Type: | Text-to-Video |
| Duration: | Up to 60 seconds |
| Realism: | Maximum (2025 benchmark) |
| Access: | Gradual rollout in ChatGPT / API |
Pros and Cons:
✅ Unmatched quality and realism. ✅ Generation of complex object interactions. ❌ Very high computational resource requirements (expensive). ❌ Availability for the general public is opening slowly.
This market segment develops in parallel with cinematic video generation. For business, online courses, and corporate training, Hollywood-level special effects are not always needed. More often, a "talking head" (Talking Head) is required—a digital narrator who can voice text in 40 languages without stuttering or demanding a fee.
Here, Lip-sync (lip synchronization) and voice cloning technology reign supreme.
HeyGen went viral thanks to its Video Translate feature, allowing bloggers to speak in perfect English, Spanish, and Japanese with their own voices. But for business, it's primarily a powerful tool for creating content without a camera.
You can create your digital double (Instant Avatar): record 2 minutes of video on a webcam, and the system creates your copy. Then you simply write the text, and the avatar speaks it. A lifesaver for experts tired of filming.
| Parameter | Value |
|---|---|
| Specialization: | Realistic avatars, video translation |
| Languages: | 40+ |
| Voice Cloning: | Yes, very accurate |
| Price: | From $24/month (Free trial available) |
| API: | Yes (for automation) |
Pros and Cons:
✅ Perfect lip-sync: lips move precisely with pronunciation. ✅ Ability to create an avatar from a photo or video. ❌ Expensive per minute of video generation on paid plans. ❌ Watermarks on the free plan.
If HeyGen is loved by bloggers, Synthesia is chosen by Fortune 500 companies. It's a platform for creating training courses, instructions, and corporate news. The library contains over 160 ready-made avatars of different races and ages.
The main feature is dialog scripts. You can seat two avatars at a table and make them talk to each other. Perfect for sales training or soft skills.
| Parameter | Value |
|---|---|
| Specialization: | Training, L&D (Learning & Development) |
| Avatars: | 160+ ready-made actors |
| Editor: | Similar to PowerPoint (slides + video) |
| Price: | From $22/month |
Pros and Cons:
✅ Convenient editor: assemble video like a presentation. ✅ High data security (SOC 2). ❌ Avatars are less emotional than HeyGen's (more "official"). ❌ Cannot create an avatar from scratch on the starter plan.
D-ID (Creative Reality Studio) specializes in animating static portraits. This is the very technology that makes a photo of your great-grandmother or the Mona Lisa move. For business, D-ID offers interactive agents—chatbots with a face that can answer clients in real-time.
Integration with Canva allows adding talking presenters directly into presentations.
| Parameter | Value |
|---|---|
| Specialization: | Photo animation, interactive agents |
| Integrations: | Canva, PowerPoint |
| Technology: | Live Portrait |
| Price: | From $5.99/month (very affordable) |
Pros and Cons:
✅ The cheapest way to make a talking head. ✅ Works with any photo (even from Midjourney). ❌ Head movement is slightly unnatural ("swaying" effect). ❌ Quality is lower than HeyGen.
Theory is good, but how does this convert into money? We've gathered real use cases demonstrating the effectiveness of implementing AI.
Problem: A seller needs to highlight a product card (e.g., a coffee maker) in the feed, but the budget for video filming with steam and beautiful lighting starts from 30,000 rubles.
Solution:
Result: The card "comes to life" in search. According to sellers, the click-through rate (CTR) of such cards is 15-20% higher compared to static images. Costs: $0 (using test credits) or $15 for a subscription.
Problem: An expert wants to enter the English-speaking market but speaks with a strong accent. Solution: Using HeyGen for content dubbing. The AI not only overlays the voice but also changes lip movement to match English speech. Result: Launching an English-language channel without reshoots. Time saved: hundreds of hours. The audience doesn't notice the substitution as the author's voice timbre is preserved.
Problem: An indie band needs a music video on a minimal budget.
Solution: Director Paul Trillo used Sora (before its public release) to create the music video "The Hardest Part." He applied the "infinite zoom" technique, flying through scenes of a couple's life: from school to old age.
Result: The video went viral and was covered by all major media worldwide. Production costs were incomparably lower than traditional filming with actors and locations.
The generative video market matured in 2025. We no longer look at "dancing monsters"; we use AI for real work: reducing advertising costs, speeding up editing, and creating content that was previously accessible only to Hollywood studios.
The main advice: don't be afraid to experiment. Technology develops faster than textbooks are written. Start with simple prompts in accessible services, and within a week, you'll be able to create videos that will amaze your clients and subscribers. The future is already here, and it's being generated at 30 frames per second.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Gemini 3 is Google DeepMind’s flagship AI model, unveiled in late 2025 as the next evolution of the Gemini lineup. Engineered as a universal multimodal intelligence, it is capable of processing text, images, audio, and video within a single, unified context.
The core objective of Gemini 3 extends beyond simple response generation; it focuses on advanced reasoning, precise information structuring, and the execution of complex task chains within the Google ecosystem.
Gemini 3 is architected as a natively multimodal model, rather than a collection of separate models stitched together by add-ons.
Core Capabilities:
Multimodal Input and Output
Enhanced Logical Reasoning
Structured Output
Agentic Capabilities
One of the definitive upgrades in Gemini 3 is its reasoning quality.
Improvements over previous versions include:
Multimodality in Practice
Gemini 3 is capable of:
This makes the model particularly valuable for analytics, education, content creation, and product documentation.
Gemini 3 Pro
Gemini 3 Flash
| Version | Speed | Analysis Depth | Primary Use Case |
|---|---|---|---|
| Pro | Medium | High | Professional tasks, Development |
| Flash | High | Medium | Search, High-volume scenarios |
Despite the significant progress, Gemini 3 has certain limitations:
State of the Market in 2025
Multimodal models have become the industry standard. AI is now directly integrated into search engines and productivity tools, while agentic capabilities are transitioning from experimental phases to concrete business cases.
Generative AI Continues to Attract Capital and Investment
In 2025, global investment in generative AI reached approximately $33.9 billion, an increase of ~18.7% compared to 2023. This reflects sustained capital inflows into the foundational layer of AI technologies.
AI Moves from Experiment to Enterprise Integration
According to analysts, many organizations have shifted from pilot projects to full-scale deployments, focusing on measurable results (ROI) and workflow automation.
Infrastructure Constraints Impact Hardware Markets Massive demand for memory and compute resources from major cloud providers is reducing the availability of DRAM/NAND for PCs and consumer devices, potentially slowing growth in the consumer hardware segment.
"AI Slop" and Content Quality – A New Management Challenge
2025 saw intensified scrutiny on low-quality generative content (often termed "AI slop"). This has raised critical questions regarding quality control and trust in AI-generated material.
AI Market Volume Continues to Expand
Forecasts indicate the global AI market will grow to approximately $757.6 billion by 2026, with a Compound Annual Growth Rate (CAGR) of ~19.2%.
Transition from "Discovery" to Mass Diffusion
Top executives at major technology firms note that 2026 will mark the year AI ceases to be an experiment and shifts toward broad, real-world integration across enterprises globally.
AI Agents and Autonomous Workflows Become Standard
Analytical reports indicate that by 2026, AI Agents will become pivotal in automating complex, multi-step business processes—moving beyond simple Q&A to executing entire tasks from start to finish.
Integration of "Physical AI" and Device-Level Automation
Consulting firms forecast that 2026 will be the year AI expands beyond the digital realm into physical systems. Autonomous robots, intelligent machines, and "synthetic perception" are becoming integral parts of industrial and service landscapes.
Dominance of Multimodal and Specialized Models
The development of models processing multiple data sources simultaneously (text + visual + audio) will continue. However, domain-specific solutions (Vertical AI) will displace "general-purpose" AI capsules where precise, context-aware conclusions are critical.
Heightened Focus on Ethics, Trust, and Regulation
As AI adoption grows, the need for transparency, explainability (XAI), and regulatory frameworks to ensure safety and social acceptance is becoming increasingly acute.
ROI and Measurable Business Outcomes as the Primary Metric
In 2026, organizations will move away from "proof of concept" pilots, demanding concrete performance indicators from AI projects: cost savings, revenue growth, and reduced turnaround times.
Economic and Investment Impacts
Analysts predict that by 2026, AI and digital transformation projects will become major drivers of economic growth. However, this may lead to asset correction and capital reallocation in adjacent sectors, including cloud infrastructure.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
